{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tomasonjo/blogs/blob/master/llm/Llamaindex-rebel-neo4j.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "752fafd9",
      "metadata": {
        "id": "752fafd9",
        "outputId": "8e806671-dc0a-4f82-a263-0df01a7464d7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting llama-index\n",
            "  Downloading llama_index-0.7.18-py3-none-any.whl (639 kB)\n",
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            "\u001b[?25hCollecting transformers\n",
            "  Downloading transformers-4.31.0-py3-none-any.whl (7.4 MB)\n",
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            "\u001b[?25hCollecting neo4j\n",
            "  Downloading neo4j-5.11.0.tar.gz (188 kB)\n",
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            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing backend dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting tiktoken (from llama-index)\n",
            "  Downloading tiktoken-0.4.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.7 MB)\n",
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            "\u001b[?25hCollecting dataclasses-json (from llama-index)\n",
            "  Downloading dataclasses_json-0.5.14-py3-none-any.whl (26 kB)\n",
            "Collecting langchain>=0.0.218 (from llama-index)\n",
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            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers)\n",
            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
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            "\u001b[?25hCollecting safetensors>=0.3.1 (from transformers)\n",
            "  Downloading safetensors-0.3.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
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            "Collecting langsmith<0.1.0,>=0.0.11 (from langchain>=0.0.218->llama-index)\n",
            "  Downloading langsmith-0.0.18-py3-none-any.whl (31 kB)\n",
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            "Collecting openapi-schema-pydantic<2.0,>=1.2 (from langchain>=0.0.218->llama-index)\n",
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            "Requirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.10/dist-packages (from sqlalchemy>=2.0.15->llama-index) (2.0.2)\n",
            "Collecting mypy-extensions>=0.3.0 (from typing-inspect>=0.8.0->llama-index)\n",
            "  Downloading mypy_extensions-1.0.0-py3-none-any.whl (4.7 kB)\n",
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            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain>=0.0.218->llama-index) (23.1.0)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain>=0.0.218->llama-index) (6.0.4)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain>=0.0.218->llama-index) (1.9.2)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain>=0.0.218->llama-index) (1.4.0)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain>=0.0.218->llama-index) (1.3.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->llama-index) (1.16.0)\n",
            "Building wheels for collected packages: neo4j\n",
            "  Building wheel for neo4j (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for neo4j: filename=neo4j-5.11.0-py3-none-any.whl size=259794 sha256=f3b0a56a5748c4ce112ef027f2ba17c17cf5a4bca0eafb753bc5e4574db637ad\n",
            "  Stored in directory: /root/.cache/pip/wheels/5f/9b/72/d893916a92e5bd362c3c28dc8dacc060419009e5a8cebb9461\n",
            "Successfully built neo4j\n",
            "Installing collected packages: tokenizers, safetensors, neo4j, mypy-extensions, marshmallow, typing-inspect, tiktoken, openapi-schema-pydantic, langsmith, huggingface-hub, transformers, openai, dataclasses-json, langchain, llama-index\n",
            "Successfully installed dataclasses-json-0.5.14 huggingface-hub-0.16.4 langchain-0.0.251 langsmith-0.0.18 llama-index-0.7.18 marshmallow-3.20.1 mypy-extensions-1.0.0 neo4j-5.11.0 openai-0.27.8 openapi-schema-pydantic-1.2.4 safetensors-0.3.1 tiktoken-0.4.0 tokenizers-0.13.3 transformers-4.31.0 typing-inspect-0.9.0\n"
          ]
        }
      ],
      "source": [
        "!pip install llama-index transformers neo4j"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "6c0a4709",
      "metadata": {
        "id": "6c0a4709",
        "outputId": "fba12771-52ed-491e-d80e-a30e06321a27",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290,
          "referenced_widgets": [
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      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cuda\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/1.42k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f710dce7a3cd4bbb9c1bf30fe06044bf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/1.63G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/1.23k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6d10fd92c0ba41c5bcd2bba1f03db672"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)in/added_tokens.json:   0%|          | 0.00/123 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9da9bcacf55c49bfa6b468e8c24698e2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/344 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "beceab44101e421f865f3945f9cd2293"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import pipeline\n",
        "import torch\n",
        "\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "print(device)\n",
        "\n",
        "triplet_extractor = pipeline(\n",
        "    'text2text-generation',\n",
        "    model='Babelscape/rebel-large',\n",
        "    tokenizer='Babelscape/rebel-large',\n",
        "    device=device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "2e1bc91d",
      "metadata": {
        "id": "2e1bc91d"
      },
      "outputs": [],
      "source": [
        "import re\n",
        "# Function to parse the generated text and extract the triplets\n",
        "# Rebel outputs a specific format. This code is mostly copied from the model card!\n",
        "\n",
        "def clean_triplets(input_text, triplets):\n",
        "    \"\"\"Sometimes the model hallucinates, so we filter out entities\n",
        "       not present in the text\"\"\"\n",
        "    text = input_text.lower()\n",
        "    clean_triplets = []\n",
        "    for triplet in triplets:\n",
        "\n",
        "        if (triplet[\"head\"] == triplet[\"tail\"]):\n",
        "            continue\n",
        "\n",
        "        head_match = re.search(\n",
        "            r'\\b' + re.escape(triplet[\"head\"].lower()) + r'\\b', text)\n",
        "        if head_match:\n",
        "            head_index = head_match.start()\n",
        "        else:\n",
        "            head_index = text.find(triplet[\"head\"].lower())\n",
        "\n",
        "        tail_match = re.search(\n",
        "            r'\\b' + re.escape(triplet[\"tail\"].lower()) + r'\\b', text)\n",
        "        if tail_match:\n",
        "            tail_index = tail_match.start()\n",
        "        else:\n",
        "            tail_index = text.find(triplet[\"tail\"].lower())\n",
        "\n",
        "        if ((head_index == -1) or (tail_index == -1)):\n",
        "            continue\n",
        "\n",
        "        clean_triplets.append((triplet[\"head\"], triplet[\"type\"], triplet[\"tail\"]))\n",
        "\n",
        "    return clean_triplets\n",
        "\n",
        "def extract_triplets(input_text):\n",
        "    text = triplet_extractor.tokenizer.batch_decode([triplet_extractor(input_text, return_tensors=True, return_text=False)[0][\"generated_token_ids\"]])[0]\n",
        "\n",
        "    triplets = []\n",
        "    relation, subject, relation, object_ = '', '', '', ''\n",
        "    text = text.strip()\n",
        "    current = 'x'\n",
        "    for token in text.replace(\"<s>\", \"\").replace(\"<pad>\", \"\").replace(\"</s>\", \"\").split():\n",
        "        if token == \"<triplet>\":\n",
        "            current = 't'\n",
        "            if relation != '':\n",
        "                triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()})\n",
        "                relation = ''\n",
        "            subject = ''\n",
        "        elif token == \"<subj>\":\n",
        "            current = 's'\n",
        "            if relation != '':\n",
        "                triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()})\n",
        "            object_ = ''\n",
        "        elif token == \"<obj>\":\n",
        "            current = 'o'\n",
        "            relation = ''\n",
        "        else:\n",
        "            if current == 't':\n",
        "                subject += ' ' + token\n",
        "            elif current == 's':\n",
        "                object_ += ' ' + token\n",
        "            elif current == 'o':\n",
        "                relation += ' ' + token\n",
        "\n",
        "    if subject != '' and relation != '' and object_ != '':\n",
        "        triplets.append({'head': subject.strip(), 'type': relation.strip(), 'tail':object_.strip()})\n",
        "    clean = clean_triplets(input_text, triplets)\n",
        "    return clean"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "efbfa609",
      "metadata": {
        "id": "efbfa609"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "# Uses openai to generate natural language responses and Cypher statements\n",
        "os.environ[\"OPENAI_API_KEY\"] = \"API-KEY\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "e59c834e",
      "metadata": {
        "id": "e59c834e"
      },
      "outputs": [],
      "source": [
        "from llama_index.graph_stores import Neo4jGraphStore\n",
        "from llama_index.storage.storage_context import StorageContext\n",
        "\n",
        "username = \"neo4j\"\n",
        "password = \"retractor-knot-thermocouples\"\n",
        "url = \"bolt://44.211.44.239:7687\"\n",
        "database = \"neo4j\"\n",
        "\n",
        "graph_store = Neo4jGraphStore(\n",
        "    username=username,\n",
        "    password=password,\n",
        "    url=url,\n",
        "    database=database,\n",
        ")\n",
        "\n",
        "storage_context = StorageContext.from_defaults(graph_store=graph_store)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "5e7b7d62",
      "metadata": {
        "id": "5e7b7d62"
      },
      "outputs": [],
      "source": [
        "from llama_index import KnowledgeGraphIndex, ServiceContext\n",
        "from llama_index import download_loader\n",
        "\n",
        "WikipediaReader = download_loader(\"WikipediaReader\")\n",
        "\n",
        "loader = WikipediaReader()\n",
        "\n",
        "documents = loader.load_data(\n",
        "    pages=[\"The Elder Scrolls V: Skyrim\"], auto_suggest=False\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "03667a2e",
      "metadata": {
        "id": "03667a2e",
        "outputId": "c18286d6-e8f3-423c-fa09-cdc743758f5d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1083: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1083: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1083: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1083: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1083: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1083: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1083: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1083: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1083: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1083: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1083: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1083: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1083: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1083: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1083: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1083: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1083: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1083: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1083: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1083: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1083: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1083: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1083: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1083: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1083: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1083: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1083: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1083: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1083: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1083: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1083: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1083: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1083: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1083: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1083: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1083: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1083: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "from llama_index.llms import OpenAI\n",
        "\n",
        "# rebel supports up to 512 input tokens, but shorter sequences also work well\n",
        "llm=OpenAI(model_name=\"gpt-3.5-turbo\")\n",
        "service_context = ServiceContext.from_defaults(llm=llm, chunk_size=256)\n",
        "\n",
        "# This can take some times\n",
        "index = KnowledgeGraphIndex.from_documents(\n",
        "    documents,\n",
        "    storage_context=storage_context,\n",
        "    kg_triplet_extract_fn=extract_triplets,\n",
        "    service_context=service_context)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "52c5de8f",
      "metadata": {
        "id": "52c5de8f",
        "outputId": "ff9fea44-d218-4554-aa7d-945bbaf44cf4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[33;1m\u001b[1;3mGraph Store Query: \n",
            "MATCH (e1:Entity)-[:HIGHEST_POINT]->(e2:Entity) \n",
            "WHERE e1.id = 'Skyrim' \n",
            "RETURN e2.id\n",
            "\u001b[0m\u001b[33;1m\u001b[1;3mGraph Store Response: [{'e2.id': 'Throat of the World'}]\n",
            "\u001b[0m\u001b[32;1m\u001b[1;3mFinal Response: \n",
            "The highest point in Skyrim is Throat of the World.\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "from llama_index.query_engine import KnowledgeGraphQueryEngine\n",
        "\n",
        "query_engine = KnowledgeGraphQueryEngine(\n",
        "    storage_context=storage_context,\n",
        "    service_context=service_context,\n",
        "    llm=llm,\n",
        "    verbose=True,\n",
        "    refresh_schema=True\n",
        ")\n",
        "\n",
        "\n",
        "response = query_engine.query(\n",
        "    \"What is the highest point in Skyrim?\",\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "352b8bf4",
      "metadata": {
        "id": "352b8bf4"
      },
      "outputs": [],
      "source": []
    }
  ],
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